Chapter 2 Data assembly

The following chapter details how we determined the area of interest and where we acquired the data products to be used in the connectivity analyses. We include justification of data sources where appropriate.

2.1 Define the focal area

This project focuses on the area covered by the Mesoamerican Biological Corridor MBC, which spans most of mainland central America. This excludes islands in central America and the Caribbean, as these will likely need a differing set of ridge-to-reef definitions. We also exclude Mexico.

The full focal area spans:

Below we show the exclusion of surrounding islands:

For an up to date assessment of the Mesoamerican Biological Corridor in Panama alone see:

Meyer, N. F., Moreno, R., Reyna-Hurtado, R., Signer, J., & Balkenhol, N. (2020). Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models. Movement ecology, 8(1), 1-14.

2.2 Data products

2.2.1 Protected areas

Shape files for protected areas were downloaded from the Protected Planet database. The World Database on Protected Areas (WDPA) is the most up-to-date and complete source of information on protected areas, updated monthly with submissions from governments, non-governmental organizations, landowners, and communities. It is managed by the United Nations Environment Programme’s World Conservation Monitoring Centre (UNEP-WCMC) with support from IUCN and its World Commission on Protected Areas (WCPA).

We buffered the area of interest by 10km, then excluded any protected ares which fell outside of that zone. This means marine protected areas >10km from the shore are not considered. There are two broad types of park - National and International designations - there are also many further subdivisions not considered here.

As recommended in the WDPA best practices guide, we removed any PA that did not report its area, or with a ‘Proposed’ status or ‘UNESCO-MAB Biosphere Reserve’ designation (Note core areas remain under national park designations).

Data source: UNEP-WCMC and IUCN (year), Protected Planet: The World Database on Protected Areas (WDPA) [February 2022], Cambridge, UK: UNEP-WCMC and IUCN Available at: Protected Planet.

And and all protected areas in an interactive version:

2.2.1.1 Types of protected area

Within our focal area, the WPDA dataset includes the following number of terrestrial and marine protected areas:

Type	Freq
Terrestrial	832
Marine	90

There are also a myriad of different protection designations:

Type	Freq
Archaeological Reserve	11
Area de Manejo de Hábitat	1
Área de Manejo de Hábitat	6
Area de Manejo de Hábitat/Especies	7
Área de Manejo de Hábitat/Especies	1
Área de Protección de Flora y Fauna	2
Área de Protección y Restauración	7
Área de Recursos Manejados	2
Area de Uso Multiple	4
Area de Uso Múltiple	5
Área de Uso Múltiple	2
Área Destinada Voluntariamente a la Conservación	4
Area Marina de Manejo	2
Área Natural	4
Área Natural Protegida	53
Área Natural Protegida Privada	1
Área Productora de Agua	2
Área Protegida con Recursos Manejados	5
Área Recreativa	2
Área Silvestre	2
Biotopo Protegido	6
Bosque Protector	2
Burdon Canal Nature Reserve	1
Cockscomb Basin Wildlife Sanctuary	1
Conservation Easement	1
Corredor Biológico	1
Crooked Tree Wildlife Sanctuary	1
Forest Reserve	15
Hol Chan	1
Humedal	10
Jardín Botánico y Centro de Investigación	1
Labouring Creek Jaguar Corridor Wildlife Sanctuary	1
Mangrove Reserve	1
Monumento Cultural	4
Monumento Historico	1
Monumento Nacional	3
Monumento Natural	7
Monumento Natural Marino	1
Mountain Pine Ridge Forest Reserve	1
National Park	15
Natural Monument	3
Nature Reserve	3
Nohoch Cheen Archaeological Reserve	1
Paisaje Protegido	4
Paisaje Terrestre Protegido	15
Parque Ecológico	1
Parque Nacional	92
Parque Nacional Marino	1
Parque Recreativo Natural Municipal	1
Parque Regional	1
Parque Regional Municipal	76
Parque Regional y Área Natural Recreativa	1
Port Honduras Marine Reserve	1
Private Reserve	8
Public Reserve	2
Ramsar Site, Wetland of International Importance	27
Refugio de Vida Silvestre	36
Refugio Nacional de Vida Silvestre	47
Reserva Antropológica y Forestal	1
Reserva Biologica	7
Reserva Biológica	13
Reserva Biósfera	2
Reserva de la Biosfera	8
Reserva de la Biósfera	4
Reserva de Recursos Genéticos	2
Reserva de Uso Multiple	1
Reserva Forestal	15
Reserva Forestal Municipal	2
Reserva Forestal Protectora de Manantiales	1
Reserva Hídrica	3
Reserva Hídrica y Forestal	1
Reserva Hidrológica	3
Reserva Natural	53
Reserva Natural Absoluta	1
Reserva Natural de la Sociedad Civil	3
Reserva Natural Privada	179
Reserva Protectora de Manantiales	1
Reservas Forestales Protectoras Nacionales	1
Reservas Naturales Privadas	13
Sin Categoría Definida	3
Sin definir	1
Sitio Ramsar, Humedal de Importancia Internacional	10
South Water Caye Marine Reserve	1
Wildlife Sanctuary	5
World Heritage Site (natural or mixed)	6
Zona de Protección Hidrológica	1
Zona de Reserva Ecológica	2
Zona de Veda Definitiva	29
Zona Protectora	30
Zona Sujeta a Conservación Ecológica	3

2.2.1.2 Marine protected areas

We will also use marine protected areas as our connectivity start points (“focal nodes”). Note - not all of these protected areas are fully marine, some span land and sea.

2.2.2 Elevation

We downloaded the elevation of the area of interest using SRTM Digital Elevation Data Version 4. The Shuttle Radar Topography Mission (SRTM) digital elevation dataset was originally produced to provide consistent, high-quality elevation data at near global scope.

Data source: Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara. 2008. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database.

2.2.3 Forest cover (current)

To get a layer reflecting current forest cover we use the Hansen Global forest Change index. These reflect results from time-series analysis of Landsat images in characterizing global forest extent and change. This data is up to date until 2021!

Possible future data incorporation:

Current (and future) coarse vegetation types can also be obtained from this Baumbach, L., Warren, D. L., Yousefpour, R., & Hanewinkel, M. (2021). Climate change may induce connectivity loss and mountaintop extinction in Central American forests. Communications Biology, 4(1), 1-12.

For an example approaches in modelling connectivity in the future (beyond the remit of this contract) see: Mozelewski, T. G., Robbins, Z. J., Scheller, R. M., & Mozelewski, T. G. (2022). Forecasting the influence of conservation strategies on landscape connectivity. Conservation Biology

2.2.4 Forest biomass

We obtained above ground biomass from Spawn, S.A., Sullivan, C.C., Lark, T.J. et al. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci Data 7, 112 (2020). This dataset provides temporally consistent and harmonized global maps of above-ground and below-ground biomass carbon density for the year 2010 at a 300-m spatial resolution.

The values represent above-ground living biomass carbon stock density of combined woody and herbaceous cover in 2010. This includes carbon stored in living plant tissues that are located above the earth’s surface (stems, bark, branches, twigs). This does not include leaf litter or coarse woody debris that were once attached to living plants but have since been deposited and are no longer living.

2.2.5 Forest height

For forest height we use the global 2005 dataset representing global tree heights based on a fusion of spaceborne-lidar data (2005) from the Geoscience Laser Altimeter System (GLAS) and ancillary geospatial data. See Simard et al. (2011) for details.

Simard, M., Pinto, N., Fisher, J., Baccini, A. 2011. Mapping forest canopy height globally with spaceborne lidar. Journal of Geophysical Research. 116: G04021

2.2.6 Mangrove cover (current)

Mangrove data is taken from the [USGS: Global Distribution of Mangroves] (https://data.unep-wcmc.org/datasets/4).

Citation: Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011). Status and distribution of mangrove forests of the world using earth observation satellite data (version 1.4, updated by UNEP-WCMC). Global Ecology and Biogeography 20: 154-159. Paper DOI: 10.1111/j.1466-8238.2010.00584.x . Data DOI: https://doi.org/10.34892/1411-w728

2.2.7 Human disturbance:

To incorporate human disturbance into the connectivity analyses we use the global Human Modification dataset (gHM). This dataset provides a cumulative measure of human modification of terrestrial lands globally at 1 square-kilometer resolution. The gHM values range from 0.0-1.0 and are calculated by estimating the proportion of a given location (pixel) that is modified, the estimated intensity of modification associated with a given type of human modification or “stressor”. They mapped 5 major anthropogenic stressors circa 2016 were mapped using 13 individual datasets:

• human settlement (population density, built-up areas)
• agriculture (cropland, livestock)
• transportation (major, minor, and two-track roads; railroads)
• mining and energy production
• electrical infrastructure (power lines, nighttime lights)

As such, this layer represents a great starting point to measure broad scale patterns in elevational gradient disturbances.

Kennedy, C.M., J.R. Oakleaf, D.M. Theobald, S. Baurch-Murdo, and J. Kiesecker. 2019. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology 00:1-16.

Examples of papers usuing the human modification index (or a derivation of it): - Gray M, Micheli E, Comendant T, Merenlender A (2020) Quantifying climate-wise connectivity across a topographically diverse landscape. Land 9:1–18. https://doi.org/10.3390/land9100355 This paper does terrestrial and riparian “permeability” - the inverse of resistance. They then compare the “cooling potential” of these corridors through looking at the pairwise difference between temperatures between linkages.

2.2.8 Current land-use and habitat

To capture land use, we are used the high resolution (10m) land cover map over Mexico and Central America created by ESA. The data are based on more than 2 years of Sentinel-2A and 2B observations from January 2016 to March 2018.

2.3 Start and end nodes

The analysis presented in the following chapters depends on having a suite of meaning start locations “reef” (reflecting mangroves, coastal protected areas and marine protected areas) and end locations “ridges” (reflecting protected high elevation forest habitats). The following code outlines the candidate start and end locations.

2.3.1 Start nodes

This is where the connectivity paths will start from:

2.3.1.1 Mangroves >1km2

According to the USGS: Global Distribution of Mangroves dataset there are 19074 discrete mangrove patches across the study area. The vast majority of these, however, are small:

The distribution of sizes (km2) is as follows:

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00000  0.00178  0.00615  0.22850  0.02601 91.29884 

Consequently, we only focus on large mangrove fragments (e.g. > 1 square kilometers) as this represents the resolution of our input layer. We have merged fragments which are very close to one another (e.g. on opposite sides of the river).

If we subset to just fragments greater than 1 km2, we have 495 fragments. The centroids of these “large mangrove” patches are distributed as follows:

2.3.1.2 Coastal Protected areas

The distribution of coastal protected areas which overlap (i.e. are within 1km) the coast is as follows:

Reading layer `Coastal_protected_areas' from data source 
  `C:\Users\cbeirne\Dropbox\GitHubProjects\Connectivity_Project\data\spatial\protected_areas\Coastal_protected_areas.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 111 features and 31 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -92.3179 ymin: 7.188517 xmax: -77.15883 ymax: 18.55661
Geodetic CRS:  WGS 84

So we have around 111 protected areas which are link ocean and land. Note this also includes near-shore marine PA’s. For each of these locations, it doesnt make sense to include the full protected area within these calculations as they sometimes run a long way inland (biasing connectivity estimates). The center points of the protected areas would not reflect reef or ocean connectivity. We therefore crop each of these protected areas to the zones within 1 km of the coast, and will use these as connectivity start points.

Map of the protected areas with 1km of the coast:

2.3.2 End nodes

End nodes represent where the connectivity paths will terminate.

2.3.2.1 High elevation protected areas

When we are measuring Ridge to Reef connectivity - we need to define a height threshold that represents a meaningful transition in elevation. What should this height be? We have initiated the analysis using a 1500m threshold, but why not use 1000m? Where we draw the line potentially influences the availability of high elevation areas which animals can move to.

Below we provide graphical representation of different height thresholds across the area of interest:

Examples of studies in central America examining elevation gradients:

Smith MA, Hallwachs W, Janzen DH (2014) Diversity and phylogenetic community structure of ants along a Costa Rican elevational gradient. Ecography (Cop) 37:720–731. https://doi.org/10.1111/j.1600-0587.2013.00631.x A study on ants - consdered “high elevation” to be between 1300 and 1600m and found high uniqueness in those high elevation sites.

From: Neate-Clegg MHC, Jones SEI, Burdekin O, et al (2018) Elevational changes in the avian community of a Mesoamerican cloud forest park. Biotropica 50:805–815. https://doi.org/10.1111/btp.12596

“Over a 10-year period, we found general increases in avian species richness and diversity at mid-to-high elevations (>1200 m), but declines at low elevations. This suggests upslope shifts in the community with lowland biotic attrition (Colwell et al. 2008)”

Deleting layer `high_elevation_pas' using driver `ESRI Shapefile'
Writing layer `high_elevation_pas' to data source 
  `data/spatial/area_of_interest/high_elevation_pas.shp' using driver `ESRI Shapefile'
Writing 502 features with 32 fields and geometry type Unknown (any).

We will use high elevation (>1500) protected areas as the “end” points for our connectivity analyses.

2.3.3 Existing corridors

2.3.3.1 Corridors according to CBM

Data obtained from the Central American Commission on Environment and Development (CCAD) - indirectly through PhD researcher Ruchi Patel rdp20@psu.edu - based on her paper: Patel, R. (2021). Paper plans and possibility: A critical analysis of landscape conservation policy in the Mesoamerican Biological Corridor. Environmental Development, 37, 100600.

The corridors span the following countries:

        Belize     Costa Rica    El Salvador      Guatemala       Honduras 
           288            560           1229            385            212 
        Mexico      Nicaragua         Panama Zona Froteriza 
           158            452            530             46 

2.3.4 Other sources

2.3.4.1 Key Biodiversity Areas

Key Biodiversity Areas (KBAs) are sites of global significance for the conservation of biodiversity. Currently there are 15,524 KBAs acknowledged worldwide, and more are continue to be identified nationally using simple, globally standardised criteria and thresholds, based on biodiversity requiring safeguards at the site scale. There are 11 criteria organized into five categories, namely (1) threatened biodiversity, (2) geographically restricted biodiversity, (3) ecological integrity, (4) biological processes, and (5) irreplaceability. As the building blocks for designing the ecosystem, bottom-up approach and maintaining effective ecological networks, Key Biodiversity Areas are the starting point for landscape-level conservation planning.

We will explore the intersection between our ridge-to-reef corridors and these designated areas.

Costa Rica: SINAC http://www.sinac.go.cr/EN-US/correbiolo/Pages/default.aspx